Security constrained unit commitment in smart energy systems: A flexibility-driven approach considering false data injection attacks in electric vehicle parking lots

• Published in: International journal of electrical power & energy systems Vol. 161; p. 110180
• Main Authors: Sharikabadi, Ramin, Abdollahi, Amir, Rashidinejad, Masoud, Shafiee, Mehdi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2024; Elsevier
• Subjects: Bi-level optimization; Electric vehicle parking lot; False data injection; Flexibility; XGBoost-based detection and correction; Electric vehicle parking lot; XGBoost-based detection and correction; False data injection; Bi-level optimization; Flexibility
• ISSN: 0142-0615
• DOI: 10.1016/j.ijepes.2024.110180

•Introducing a new FBSCUCFDIP-P/EV structure as flexibility-based security constrained unit commitment in the presence of false data injection attack in to the communication infrastructure of EVs.•Examining the flexible-oriented approach of uncertain EVs in SCUC with the aim of reducing the operating cost and increasing the flexibility indices.•Proposing an operator-adversary bilevel optimization problem to evaluate the impact of false data injection attacks on the electric vehicles parking lots in the flexibility-based SCUC.•Applying a defense mechanism using XGBoost-based machine learning approach to detect and correct false data injected into electric vehicles parking lots. In this paper, a new structure, the so-called FBSCUCFDIP-P/EV is proposed as flexibility-based security constrained unit commitment (SCUC) in the presence of false data injection (FDI) attack into the communication infrastructure of electric vehicle parking lot (EVPL). Herein, the uncertain EVPLs are integrated into the SCUC problem with the aim of reducing the operation cost. It is notable that growing integration of unspecified EVPLs can introduce novel challenges to the power system, significantly impacting its flexibility. In this study, electric vehicles are leveraged as a means to enhance system flexibility. Meanwhile, the FDI attacks in EVPLs can distort the system’s flexibility and lead to inaccurate assessments of the power system’s ability to adapt to changing conditions. In order to model the FDI attack, a bi-level optimization problem based on mixed integer linear programming is formulated. At the upper level, the impact of EVPLs on the flexibility indices of the SCUC is evaluated, and the false data injected into the EVPL is calculated at the lower level. Since both levels of the proposed FBSCUCFDIP-P/EV include discrete variables, a reformulation and decomposition technique is utilized to achieve the optimal solution. Instead, an extreme gradient boosting (XGBoost)-based machine learning method is considered to detection and correction of FDI attack. The proposed approach is tested on the IEEE 24-bus system. The simulation results initially indicate the improvement of the flexibility of the power system in proposed structure. Further, injecting false data into all available EVPLs causes to increase the system operation cost. Besides, false data leads to distorted charging and discharging scheduling of EVPLs; likewise, scheduling and commitment of power generation units also changes. Subsequently, the application of the XGBoost algorithm effectively mitigates the impact of FDI attacks, achieving a maximum accuracy of 85.41%.